Objectives

The primary objectives of this analysis is determine if there is a trend in standing stock of calcareous green algae, Penicillus, from 2007-2015 in Florida Bay. The anaylsis will also develop explanatory models using temperature and salinity.

Methods

Site Information

Florida Bay is a subtropical lagoon system with large expanses of seagrass beds and macroalgae. Biomass data for Penicillus spp. was collected as a proxy for organic carbon from Duck Key (TS/Ph9) in Florida Bay (Figure 1). Duck Key is located in the north eastern region of Florida Bay which is characterized by brackish waters with low salinity compared to other regions of the Bay. This site has low algal species diversity with only Penicillus present. Due to its location close to the watershed, there is typically higher nitrogen and phosphorus limitation at this site.

Figure 1 showing study sites at Florida Bay

Figure 1 showing study sites at Florida Bay

Statistical Analysis

Results

## Time Series:
## Start = c(1, 1) 
## End = c(9, 6) 
## Frequency = 6 
##  [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.9360000 0.5100000 0.7990000
##  [8] 0.5746667 1.2146667 0.6706667 1.0186667 0.1940000 0.5986667 0.1940000
## [15] 0.7136667 0.3453333 0.6826667 1.3000000 0.6080000 0.9700000 0.3333333
## [22] 0.2786667 0.8933333 0.9986667 1.0200000 1.2573333 0.1560000 0.0000000
## [29] 0.8973333 1.3706667 0.6333333 0.8680000 0.0000000 0.0000000 0.0000000
## [36] 0.0000000 0.0000000 0.0000000 0.0000000 0.7506667 0.5160000 0.0000000
## [43] 0.0000000 0.3546667 0.0000000 0.0000000 0.9773333 0.0000000 0.3560000
## [50] 6.0920000 0.0000000 0.0000000 1.9013333 0.0000000

## Time Series:
## Start = c(1, 1) 
## End = c(9, 6) 
## Frequency = 6 
##  [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.9360000 0.5100000 0.7990000
##  [8] 0.5746667 1.2146667 0.6706667 1.0186667 0.1940000 0.5986667 0.1940000
## [15] 0.7136667 0.3453333 0.6826667 1.3000000 0.6080000 0.9700000 0.3333333
## [22] 0.2786667 0.8933333 0.9986667 1.0200000 1.2573333 0.1560000 0.0000000
## [29] 0.8973333 1.3706667 0.6333333 0.8680000 0.0000000 0.0000000 0.0000000
## [36] 0.0000000 0.0000000 0.0000000 0.0000000 0.7506667 0.5160000 0.0000000
## [43] 0.0000000 0.3546667 0.0000000 0.0000000 0.9773333 0.0000000 0.3560000
## [50] 0.4576780 0.0000000 0.0000000 1.9013333 0.0000000

## 
##  Augmented Dickey-Fuller Test
## 
## data:  nee1
## Dickey-Fuller = -3.9814, Lag order = 3, p-value = 0.01699
## alternative hypothesis: stationary

## 
##  ARIMA(2,0,2)(1,0,1)[6] with non-zero mean : Inf
##  ARIMA(0,0,0)           with non-zero mean : 76.37768
##  ARIMA(1,0,0)(1,0,0)[6] with non-zero mean : 71.5361
##  ARIMA(0,0,1)(0,0,1)[6] with non-zero mean : 73.00318
##  ARIMA(0,0,0)           with zero mean     : 111.3671
##  ARIMA(1,0,0)           with non-zero mean : 77.09326
##  ARIMA(1,0,0)(2,0,0)[6] with non-zero mean : 73.714
##  ARIMA(1,0,0)(1,0,1)[6] with non-zero mean : 73.8068
##  ARIMA(1,0,0)(0,0,1)[6] with non-zero mean : 72.41139
##  ARIMA(1,0,0)(2,0,1)[6] with non-zero mean : 76.13598
##  ARIMA(0,0,0)(1,0,0)[6] with non-zero mean : 72.31195
##  ARIMA(2,0,0)(1,0,0)[6] with non-zero mean : 73.08038
##  ARIMA(1,0,1)(1,0,0)[6] with non-zero mean : 73.41031
##  ARIMA(0,0,1)(1,0,0)[6] with non-zero mean : 72.2416
##  ARIMA(2,0,1)(1,0,0)[6] with non-zero mean : 75.60375
##  ARIMA(1,0,0)(1,0,0)[6] with zero mean     : 75.22356
## 
##  Best model: ARIMA(1,0,0)(1,0,0)[6] with non-zero mean

## [1] 70.71978

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,0,0)(1,0,0)[6] with non-zero mean
## Q* = 25.027, df = 33, p-value = 0.8389
## 
## Model df: 3.   Total lags used: 36

##     Order   Date   Biomass Turbidity Temperature Salinity
## 1  2007.1 Feb-07 0.0000000       4.1        24.7     36.6
## 2  2007.2 Apr-07 0.0000000       1.4        24.9     35.1
## 3  2007.3 Jun-07 0.0000000       1.2        25.3     34.8
## 4  2007.4 Aug-07 0.0000000       0.4        32.0     28.3
## 5  2007.5 Oct-07 0.9360000       0.6        25.7     25.9
## 6  2007.6 Dec-07 0.5100000       0.8        25.6     26.8
## 7  2008.1 Feb-08 0.7990000       0.9        22.6     28.1
## 8  2008.2 Apr-08 0.5746667       1.4        26.4     31.8
## 9  2008.3 Jun-08 1.2146667       0.5        29.9     38.7
## 10 2008.4 Aug-08 0.6706667       0.5        32.8     39.1
## 11 2008.5 Oct-08 1.0186667       0.9        29.3     33.8
## 12 2008.6 Dec-08 0.1940000       0.7        22.1     31.8
## 13 2009.1 Feb-09 0.5986667       3.5        21.2     34.0
## 14 2009.2 Apr-09 0.1940000       8.4        27.1     36.4
## 15 2009.3 Jun-09 0.7136667       0.2        31.0     35.3
## 16 2009.4 Aug-09 0.3453333       0.1        30.8     38.4
## 17 2009.5 Oct-09 0.6826667       1.2        30.2     30.2
## 18 2009.6 Dec-09 1.3000000       1.4        25.1     30.4
## 19 2010.1 Feb-10 0.6080000       3.8        21.4     26.3
## 20 2010.2 Apr-10 0.9700000       3.5        26.8     27.7
## 21 2010.3 Jun-10 0.3333333       0.5        31.8     33.8
## 22 2010.4 Aug-10 0.2786667       1.6        29.3     34.8
## 23 2010.5 Oct-10 0.8933333   -9999.0        20.5     24.5
## 24 2010.6 Dec-10 0.9986667       2.7        17.5     23.9
## 25 2011.1 Feb-11 1.0200000       2.3        22.3     23.9
## 26 2011.2 Apr-11 1.2573333   -9999.0        28.4     38.3
## 27 2011.3 Jun-11 0.1560000       1.4        28.7     39.7
## 28 2011.4 Aug-11 0.0000000       0.6        30.8     40.8
## 29 2011.5 Oct-11 0.8973333       0.8        26.9     25.4
## 30 2011.6 Dec-11 1.3706667       2.1        22.5     22.4
## 31 2012.1 Feb-12 0.6333333       0.9        20.4     30.6
## 32 2012.2 Apr-12 0.8680000       0.8        24.6     33.2
## 33 2012.3 Jun-12 0.0000000       3.3        31.6     22.6
## 34 2012.4 Aug-12 0.0000000       1.1        31.0     21.8
## 35 2012.5 Oct-12 0.0000000   -9999.0        29.8     22.4
## 36 2012.6 Dec-12 0.0000000       1.3        26.3     24.8
## 37 2013.1 Feb-13 0.0000000       1.3        23.2     27.3
## 38 2013.2 Apr-13 0.0000000   -9999.0        28.7     32.0
## 39 2013.3 Jun-13 0.0000000   -9999.0        29.2     33.2
## 40 2013.4 Aug-13 0.7506667       0.2        30.7     23.4
## 41 2013.5 Oct-13 0.5160000   -9999.0        27.7     31.0
## 42 2013.6 Dec-13 0.0000000   -9999.0        26.3     30.3
## 43 2014.1 Feb-14 0.0000000   -9999.0        23.9     28.2
## 44 2014.2 Apr-14 0.3546667       1.1        26.1     31.7
## 45 2014.2 Jun-14 0.0000000       8.2        29.4     38.1
## 46 2014.3 Aug-14 0.0000000       0.0        32.8     34.9
## 47 2014.4 Oct-14 0.9773333       0.6        27.9     37.7
## 48 2014.5 Dec-14 0.0000000       4.9        23.1     35.5
## 49 2015.1 Feb-15 0.3560000   -9999.0        19.6     35.7
## 50 2015.2 Apr-15 6.0920000   -9999.0        29.4     39.2
## 51 2015.3 Jun-15 0.0000000       0.6        30.7     42.0
## 52 2015.4 Aug-15 0.0000000       0.3        31.0     47.8
## 53 2015.5 Oct-15 1.9013333       0.4        26.8     38.9
## 54 2015.6 Dec-15 0.0000000   -9999.0        29.1     37.5

## 
##  Augmented Dickey-Fuller Test
## 
## data:  temp
## Dickey-Fuller = -4.1109, Lag order = 3, p-value = 0.0114
## alternative hypothesis: stationary

## 
##  ARIMA(2,0,2)(1,0,1)[6] with non-zero mean : Inf
##  ARIMA(0,0,0)           with non-zero mean : 78.259
##  ARIMA(1,0,0)(1,0,0)[6] with non-zero mean : 73.54395
##  ARIMA(0,0,1)(0,0,1)[6] with non-zero mean : 75.07276
##  ARIMA(0,0,0)           with zero mean     : 113.2427
##  ARIMA(1,0,0)           with non-zero mean : 78.82628
##  ARIMA(1,0,0)(2,0,0)[6] with non-zero mean : 75.95254
##  ARIMA(1,0,0)(1,0,1)[6] with non-zero mean : 75.99753
##  ARIMA(1,0,0)(0,0,1)[6] with non-zero mean : 74.51044
##  ARIMA(1,0,0)(2,0,1)[6] with non-zero mean : 78.52109
##  ARIMA(0,0,0)(1,0,0)[6] with non-zero mean : 74.37048
##  ARIMA(2,0,0)(1,0,0)[6] with non-zero mean : 75.38997
##  ARIMA(1,0,1)(1,0,0)[6] with non-zero mean : 75.60254
##  ARIMA(0,0,1)(1,0,0)[6] with non-zero mean : 74.19889
##  ARIMA(2,0,1)(1,0,0)[6] with non-zero mean : Inf
##  ARIMA(1,0,0)(1,0,0)[6] with zero mean     : 77.25348
## 
##  Best model: Regression with ARIMA(1,0,0)(1,0,0)[6] errors
##            df      AIC
## arima.nee2  4 70.71978
## arima.nee3  5 72.29395

## 
##  ARIMA(2,0,2)(1,0,1)[6] with non-zero mean : Inf
##  ARIMA(0,0,0)           with non-zero mean : 74.83528
##  ARIMA(1,0,0)(1,0,0)[6] with non-zero mean : 70.79061
##  ARIMA(0,0,1)(0,0,1)[6] with non-zero mean : 71.30411
##  ARIMA(0,0,0)           with zero mean     : 84.30107
##  ARIMA(1,0,0)           with non-zero mean : 74.13554
##  ARIMA(1,0,0)(2,0,0)[6] with non-zero mean : 71.367
##  ARIMA(1,0,0)(1,0,1)[6] with non-zero mean : 72.41748
##  ARIMA(1,0,0)(0,0,1)[6] with non-zero mean : 70.04039
##  ARIMA(1,0,0)(0,0,2)[6] with non-zero mean : 72.08987
##  ARIMA(1,0,0)(1,0,2)[6] with non-zero mean : 74.05626
##  ARIMA(0,0,0)(0,0,1)[6] with non-zero mean : 72.14134
##  ARIMA(2,0,0)(0,0,1)[6] with non-zero mean : 71.10069
##  ARIMA(1,0,1)(0,0,1)[6] with non-zero mean : 71.65452
##  ARIMA(2,0,1)(0,0,1)[6] with non-zero mean : 73.70728
##  ARIMA(1,0,0)(0,0,1)[6] with zero mean     : 72.10366
## 
##  Best model: Regression with ARIMA(1,0,0)(0,0,1)[6] errors
##            df      AIC
## arima.nee2  4 70.71978
## arima.nee4  5 68.79039

## 
##  Ljung-Box test
## 
## data:  Residuals from Regression with ARIMA(1,0,0)(0,0,1)[6] errors
## Q* = 25.494, df = 32, p-value = 0.7855
## 
## Model df: 4.   Total lags used: 36

## 
##  Augmented Dickey-Fuller Test
## 
## data:  sal
## Dickey-Fuller = -1.8338, Lag order = 3, p-value = 0.6414
## alternative hypothesis: stationary

## 
##  ARIMA(2,0,2)(1,0,1)[6] with non-zero mean : 80.84753
##  ARIMA(0,0,0)           with non-zero mean : 76.5879
##  ARIMA(1,0,0)(1,0,0)[6] with non-zero mean : 71.38143
##  ARIMA(0,0,1)(0,0,1)[6] with non-zero mean : 73.28203
##  ARIMA(0,0,0)           with zero mean     : 112.4782
##  ARIMA(1,0,0)           with non-zero mean : 77.33439
##  ARIMA(1,0,0)(2,0,0)[6] with non-zero mean : 73.6196
##  ARIMA(1,0,0)(1,0,1)[6] with non-zero mean : 73.74603
##  ARIMA(1,0,0)(0,0,1)[6] with non-zero mean : 72.4739
##  ARIMA(1,0,0)(2,0,1)[6] with non-zero mean : 76.06255
##  ARIMA(0,0,0)(1,0,0)[6] with non-zero mean : 72.5068
##  ARIMA(2,0,0)(1,0,0)[6] with non-zero mean : 72.54572
##  ARIMA(1,0,1)(1,0,0)[6] with non-zero mean : 73.14711
##  ARIMA(0,0,1)(1,0,0)[6] with non-zero mean : 72.32733
##  ARIMA(2,0,1)(1,0,0)[6] with non-zero mean : 75.17466
##  ARIMA(1,0,0)(1,0,0)[6] with zero mean     : 74.72702
## 
##  Best model: Regression with ARIMA(1,0,0)(1,0,0)[6] errors
##            df      AIC
## arima.nee2  4 70.71978
## arima.nee5  5 70.13143

## 
##  ARIMA(2,0,2)(1,0,1)[6] with non-zero mean : Inf
##  ARIMA(0,0,0)           with non-zero mean : 78.22982
##  ARIMA(1,0,0)(1,0,0)[6] with non-zero mean : 73.48935
##  ARIMA(0,0,1)(0,0,1)[6] with non-zero mean : 75.36744
##  ARIMA(0,0,0)           with zero mean     : 87.41693
##  ARIMA(1,0,0)           with non-zero mean : 78.89038
##  ARIMA(1,0,0)(2,0,0)[6] with non-zero mean : 75.9331
##  ARIMA(1,0,0)(1,0,1)[6] with non-zero mean : 75.97044
##  ARIMA(1,0,0)(0,0,1)[6] with non-zero mean : 74.69141
##  ARIMA(1,0,0)(2,0,1)[6] with non-zero mean : 78.67409
##  ARIMA(0,0,0)(1,0,0)[6] with non-zero mean : 74.57345
##  ARIMA(2,0,0)(1,0,0)[6] with non-zero mean : 74.75671
##  ARIMA(1,0,1)(1,0,0)[6] with non-zero mean : 75.29719
##  ARIMA(0,0,1)(1,0,0)[6] with non-zero mean : 74.41315
##  ARIMA(2,0,1)(1,0,0)[6] with non-zero mean : Inf
##  ARIMA(1,0,0)(1,0,0)[6] with zero mean     : 77.46904
## 
##  Best model: Regression with ARIMA(1,0,0)(1,0,0)[6] errors
##            df      AIC
## arima.nee2  4 70.71978
## arima.nee6  5 72.23935

## 
##  Ljung-Box test
## 
## data:  Residuals from Regression with ARIMA(1,0,0)(1,0,0)[6] errors
## Q* = 29.373, df = 32, p-value = 0.6001
## 
## Model df: 4.   Total lags used: 36

Discussion